Blog Data Showdown: Machine Learning vs Neuro-Symbolic Models for Gender Classification

TL;DR

This paper compares traditional machine learning models, deep learning, and neuro-symbolic AI for gender classification from blog text, analyzing various text representations and feature extraction methods to evaluate their effectiveness.

Contribution

It provides a comprehensive comparative analysis of machine learning, deep learning, and neuro-symbolic models for text classification, highlighting the potential of NeSy approaches with limited data.

Findings

NeSy approach matched strong MLP results despite limited data

Different text representations significantly impact model performance

Feature extraction techniques influence classification accuracy

Abstract

Text classification problems, such as gender classification from a blog, have been a well-matured research area that has been well studied using machine learning algorithms. It has several application domains in market analysis, customer recommendation, and recommendation systems. This study presents a comparative analysis of the widely used machine learning algorithms, namely Support Vector Machines (SVM), Naive Bayes (NB), Logistic Regression (LR), AdaBoost, XGBoost, and an SVM variant (SVM_R) with neuro-symbolic AI (NeSy). The paper also explores the effect of text representations such as TF-IDF, the Universal Sentence Encoder (USE), and RoBERTa. Additionally, various feature extraction techniques, including Chi-Square, Mutual Information, and Principal Component Analysis, are explored. Building on these, we introduce a comparative analysis of the machine learning and deep learning approaches in comparison to the NeSy. The experimental results show that the use of the NeSy approach matched strong MLP results despite a limited dataset. Future work on this research will expand the knowledge base, the scope of embedding types, and the hyperparameter configuration to further study the effectiveness of the NeSy approach.